Reducing Bias in Estimates of Per Protocol Treatment Effects

This secondary analysis of a randomized clinical trial evaluates ways of reducing bias in estimates of per protocol treatment effects.


Introduction
Intent-to-treat effect estimates from randomized trials are influenced by participant adherence to the treatment protocol. When protocol adherence is imperfect, estimation of per-protocol effects is recommended. 1,2 Frequently, per-protocol effects are estimated by censoring or excluding participants who deviate from a protocol (Table). This approach provides valid estimates if protocol deviations are noninformative (ie, completely at random within treatment groups).
However, protocol deviations can be informative, especially when deviations are caused by factors associated with risk for the outcome. Reporting per-protocol effects without accounting for the possible bias induced by censoring protocol deviations is analogous to reporting an observational study with no control for confounding. To minimize the potential for such bias, one can account for protocol deviations using a per-protocol estimator that standardizes for observed variables with inverse probability weights, 3 generalized computation, 4 or comparable approaches.

Methods
In this secondary analysis of a randomized clinical trial, we illustrate how failing to account for informative protocol deviations can lead to biased estimates using data from a trial of 17 alphahydroxyprogesterone caproate (17P) to reduce preterm birth among HIV positive pregnant people.

+ Supplemental content
Author affiliations and article information are listed at the end of this article. To illustrate the potential for bias arising from differential protocol deviations, suppose contrary to fact, cervical length was also a predictor of protocol deviation, such that 152 of 186 (81%) participants in the 17P group with a cervix less than 4 cm deviated from the protocol but only 95 of the remaining 614 (15%) participants deviated from the protocol. Under such a scenario, if we censor protocol deviants as did the studies in the

Discussion
When protocol deviations are not completely at random within treatment groups, an unstandardized per-protocol effect estimator can be biased, even grossly, as shown in the example. The example enjoys all the benefits of counterexamples but has the limitations of being partially contrived and having restricted scope. Using postrandomization information on protocol adherence to censor participants requires accounting for possible induced bias. Standardized per-protocol effect estimators will generally be valid if the common causes of protocol deviations and outcomes are measured and accounted for appropriately. 1,2 However, typical regression models fail to account for postrandomization variables appropriately even when a sufficient set of variables is measured. 1 But standardization methods such as inverse probability weighting and generalized computation can provide valid per-protocol effect estimates when a sufficient set of variables is measured. 3,4 Although there is 1 intent-to-treat effect (for the observed amount of adherence), there are as many per protocol effects as there are possible definitions of protocol deviation. In the 17P example previously mentioned, participants were classified as deviating from protocol when they missed more than 1 weekly injection, but protocol deviations could have been defined less stringently as, for example, more than 3 missed injections. In conclusion, we recommend randomized trials report the estimated intent-to-treat effect, and when protocol deviations occur, a series of standardized per-protocol effect estimates which vary the stringency of the definition of a protocol deviation.